Analyzing data differences due to changes made in the system is often very difficult, especially for multi-dimensional datasets, such as online analytical processing (OLAP) based multi-dimensional datasets. In computing, OLAP is an approach to answering multi-dimensional analytical queries swiftly. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business processing management, budgeting and forecasting, financial reporting and similar areas.
Currently, in attempting to quantify or understand the effects to the multi-dimensional data from changes made in the system (e.g., using version 2 instead of version 1, adding/deleting/modifying business rules, month of January versus the month of April), the user will review and compare the data, such as comparing the data in two tables side by side. However, such a process is time-consuming and inefficient and perhaps error-prone in quantifying or understanding the effects to the data from changes made in the system.